Abstract: As picture fuzzy clustering algorithm is not suitable for segmentation of image with noise or inhomogeneous intensity, an adaptive robust picture fuzzy clustering segmentation algorithm based on total Bregman divergence is proposed. An improved total Bregman divergence is constructed by combination of existing total Bregman divergence and neighborhood information of image pixel, which is suitable for image segmentation. It was introduced into the picture fuzzy c-means clustering optimization model, and a robust total Bregman divergence-based picture fuzzy clustering algorithm, in which the pixel spatial neighborhood information was embedded, was obtained. The difference between the gray values of current clustering pixel and its neighborhood pixel is used as the regularization factor of the robust picture fuzzy clustering model based total Bregman divergence, and thus the robust clustering segmentation method would be capable of suppressing the noise adaptively. The results show that the segmentation quality and anti-noise robustness of the proposed segmentation algorithm are improved more significantly than those of the existing picture fuzzy clustering and other robust fuzzy clustering algorithms. Key

Key words: imagesegmentation, picturefuzzyset, picturefuzzyclustering, totalBregmandivergence, adaptation, robustness, c-meansclustering